本文以 Elasticsearch 5.6.2爲例。
最新(截止到2018-09-23)的 Elasticsearch 是 6.4.1。5.x
系列和6.x
系列雖然有些區別,但基本用法是一樣的。
官方文檔:
https://www.elastic.co/guide/...
安裝
安裝比較簡單。分兩步:
- 配置JDK環境
- 安裝Elasticsearch
Elasticsearch 依賴 JDK環境,需要系統先下載安裝 JDK 並配置 JAVA_HOME
環境變量。JDK 版本推薦:1.8.0系列。地址:https://www.oracle.com/techne...
安裝JDk
Linux:
$ yum install -y java-1.8.0-openjdk
配置環境變量,需要修改/etc/profile
, 增加:
JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.181-3.b13.el6_10.x86_64
PATH=$JAVA_HOME/bin:$PATH
CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
JAVACMD=/usr/bin/java
export JAVA_HOME JAVACMD CLASSPATH PATH
然後使之生效:
source /etc/profile
Windows:
安裝包地址:
http://download.oracle.com/ot...
下載並配置JDK環境變量
JAVA_HOME=C:\Program Files\Java\jdk1.8.0_101
CLASSPATH=.;%JAVA_HOME%\lib;.;%JAVA_HOME%\lib\dt.jar;%JAVA_HOME%\lib\tools.jar;
安裝Elasticsearch
Elasticsearch 安裝只需要下載二進制壓縮包包,解壓即可使用。需要特別注意的是版本號,如果還要安裝Kibana及插件,需要注意選用一樣的版本號。
安裝包下載:https://artifacts.elastic.co/...
這個頁面有 Elasticsearch 所有版本的下載:https://www.elastic.co/downlo...
下載後解壓到指定目錄,進入到 bin 目錄,就可以運行 Elasticsearch 了:
Linux:
./elasticsearch
Windows:
elasticsearch.bat
注: Linux/Mac環境不能使用 root 用戶運行。
基礎入門
我們可以使用curl或者kibana提供的Dev Tools進行API測試。
例如:
curl方式:
curl 'localhost:9200/_cat/health?format=json'
[{"epoch":"1537689647","timestamp":"16:00:47","cluster":"elasticsearch","status":"yellow","node.total":"1","node.data":"1","shards":"11","pri":"11","relo":"0","init":"0","unassign":"11","pending_tasks":"0","max_task_wait_time":"-","active_shards_percent":"50.0%"}]
Dev Tools:
GET /_cat/health?format=json
個人比較喜歡Kibana提供的Dev Tools,非常方便。
查看_cat
命令:
GET _cat
=^.^=
/_cat/allocation
/_cat/shards
/_cat/shards/{index}
/_cat/master
/_cat/nodes
/_cat/tasks
/_cat/indices
/_cat/indices/{index}
/_cat/segments
/_cat/segments/{index}
/_cat/count
/_cat/count/{index}
/_cat/recovery
/_cat/recovery/{index}
/_cat/health
/_cat/pending_tasks
/_cat/aliases
/_cat/aliases/{alias}
/_cat/thread_pool
/_cat/thread_pool/{thread_pools}
/_cat/plugins
/_cat/fielddata
/_cat/fielddata/{fields}
/_cat/nodeattrs
/_cat/repositories
/_cat/snapshots/{repository}
/_cat/templates
以下測試均在Dev Tools執行。
節點操作
查看健康狀態
GET /_cat/health?format=json
結果:
[
{
"epoch": "1537689915",
"timestamp": "16:05:15",
"cluster": "elasticsearch",
"status": "yellow",
"node.total": "1",
"node.data": "1",
"shards": "11",
"pri": "11",
"relo": "0",
"init": "0",
"unassign": "11",
"pending_tasks": "0",
"max_task_wait_time": "-",
"active_shards_percent": "50.0%"
}
]
健康狀態有3種:
- Green - 正常(集羣功能齊全)
- Yellow - 所有數據均可用,但尚未分配一些副本(羣集功能齊全)
- Red - 某些數據由於某種原因不可用(羣集部分功能可用)
注意:當羣集爲紅色時,它將繼續提供來自可用分片的搜索請求,但您可能需要儘快修復它,因爲存在未分配的分片。
查看節點
GET /_cat/nodes?format=json
索引
查看所有index
GET /_cat/indices?format=json
結果:
[
{
"health": "yellow",
"status": "open",
"index": "filebeat-2018.09.23",
"uuid": "bwWVhUkBTIe46h9QJfmZHw",
"pri": "5",
"rep": "1",
"docs.count": "4231",
"docs.deleted": "0",
"store.size": "2.5mb",
"pri.store.size": "2.5mb"
},
{
"health": "yellow",
"status": "open",
"index": ".kibana",
"uuid": "tnWbNLSMT7273UEh6RfcBg",
"pri": "1",
"rep": "1",
"docs.count": "4",
"docs.deleted": "0",
"store.size": "23.9kb",
"pri.store.size": "23.9kb"
}
]
創建index
PUT /customer?pretty
刪除index
DELETE /customer?pretty
查詢指定 Index 的 mapping
GET /customer/_mapping?pretty
注:ElasticSearch裏面有index
和type
的概念:index稱爲索引,type爲文檔類型,一個index下面有多個type,每個type的字段可以不一樣。這類似於關係型數據庫的 database 和 table 的概念。但是,ES中不同type下名稱相同的filed最終在Lucene中的處理方式是一樣的。所以後來ElasticSearch團隊想去掉type,於是在6.x版本爲了向下兼容,一個index只允許有一個type。預計7.x版本徹底去掉type。參考:https://www.elastic.co/guide/...所以,實際使用中建議一個
index
裏面僅有一個type
,名稱可以和index一致,或者使用固定的doc
。
增刪改查
按ID新增數據
type爲doc:
PUT /customer/doc/1?pretty
{
"name": "John Doe"
}
PUT /customer/doc/2?pretty
{
"name": "yujc",
"age":22
}
如果index不存在,直接新增數據也會同時創建index。
同時,該操作也能修改數據:
PUT /customer/doc/2?pretty
{
"name": "yujc",
"age":23
}
age
字段會被修改,而且_version會被修改爲2:
{
"_index": "customer",
"_type": "doc",
"_id": "1",
"_version": 2,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": false
}
按ID查詢數據
GET /customer/doc/1?pretty
結果:
{
"_index": "customer",
"_type": "doc",
"_id": "1",
"_version": 2,
"found": true,
"_source": {
"name": "John Doe"
}
}
直接新增數據
我們也可以不指定文檔ID從而直接新增數據:
POST /customer/doc?pretty
{
"name": "yujc",
"age":23
}
注意這裏使用的動作是POST
。PUT
新增數據必須指定文檔ID。
更新數據
我們使用下面兩種方式均能更新已有數據:
PUT /customer/doc/1?pretty
{
"name": "yujc2",
"age":22
}
POST /customer/doc/1?pretty
{
"name": "yujc2",
"age":22
}
以上操作均會覆蓋現有數據。
如果只是想更新指定字段,必須使用POST
加參數的形式:
POST /customer/doc/1/_update?pretty
{
"doc":{"name": "yujc"}
}
其中_update
表示更新。doc
必須有,否則會報錯。
增加字段:
POST /customer/doc/1/_update?pretty
{
"doc":{"yeat": 2018}
}
就會在已有的數據基礎上增加一個year
字段,不會覆蓋已有數據:
GET /customer/doc/1?pretty
結果:
{
"_index": "customer",
"_type": "doc",
"_id": "1",
"_version": 16,
"found": true,
"_source": {
"name": "yujc",
"age": 22,
"yeat": 2018
}
}
也可以使用簡單腳本執行更新。此示例使用腳本將年齡增加5:
POST /customer/doc/1/_update?pretty
{
"script":"ctx._source.age+=5"
}
結果:
{
"_index": "customer",
"_type": "doc",
"_id": "1",
"_version": 17,
"found": true,
"_source": {
"name": "yujc",
"age": 27,
"yeat": 2018
}
}
按ID刪除數據
DELETE /customer/doc/1?pretty
批量
新增
POST /customer/doc/_bulk?pretty
{"index":{"_id":"1"}}
{"name": "John Doe" }
{"index":{"_id":"2"}}
{"name": "Jane Doe" }
該操作會新增2條記錄,而不是4條。查詢數據:
GET /customer/doc/2?pretty
結果:
{
"_index": "customer",
"_type": "doc",
"_id": "2",
"_version": 2,
"found": true,
"_source": {
"name": "Jane Doe"
}
}
更新、刪除
POST /customer/doc/_bulk?pretty
{"update":{"_id":"1"}}
{"doc": { "name": "John Doe becomes Jane Doe" } }
{"delete":{"_id":"2"}}
該操作會更新ID爲1的文檔,刪除ID爲2的文檔。
注意:批量操作如果某條失敗了,並不影響下一條繼續執行。
防盜版聲明:本文系原創文章,發佈於公衆號飛鴻影的博客
(fhyblog)及博客園,轉載需作者同意。
全文檢索
經過前面的基礎入門,我們對ES的基本操作也會了。現在來學習ES最強大的部分:全文檢索。
準備工作
批量導入數據
先需要準備點數據,然後導入:
wget https://raw.githubusercontent.com/elastic/elasticsearch/master/docs/src/test/resources/accounts.json
curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/account/_bulk?pretty&refresh" --data-binary "@accounts.json"
這樣我們就導入了1000條數據到ES。index是bank。我們可以查看現在有哪些index:
curl "localhost:9200/_cat/indices?format=json&pretty"
結果:
[
{
"health" : "yellow",
"status" : "open",
"index" : "bank",
"uuid" : "IhyOzz3WTFuO5TNgPJUZsw",
"pri" : "5",
"rep" : "1",
"docs.count" : "1000",
"docs.deleted" : "0",
"store.size" : "640.3kb",
"pri.store.size" : "640.3kb"
},
{
"health" : "yellow",
"status" : "open",
"index" : "customer",
"uuid" : "f_nzBLypSUK2SVjL2AoKxQ",
"pri" : "5",
"rep" : "1",
"docs.count" : "9",
"docs.deleted" : "0",
"store.size" : "31kb",
"pri.store.size" : "31kb"
},
{
"health" : "yellow",
"status" : "open",
"index" : ".kibana",
"uuid" : "tnWbNLSMT7273UEh6RfcBg",
"pri" : "1",
"rep" : "1",
"docs.count" : "5",
"docs.deleted" : "0",
"store.size" : "29.4kb",
"pri.store.size" : "29.4kb"
}
]
使用kibana可視化數據
該小節是可選的,如果不感興趣,可以跳過。
該小節要求你已經搭建好了ElasticSearch + Kibana。
打開kibana web地址:http://127.0.0.1:5601,依次打開:Management
-> Kibana
-> Index Patterns
,選擇Create Index Pattern
:
a. Index pattern 輸入:bank
;
b. 點擊Create。
然後打開Discover,選擇 bank
就能看到剛纔導入的數據了。
我們在可視化界面裏檢索數據:
是不是很酷!
接下來我們使用API來實現檢索。
關鍵字檢索
模糊檢索
GET /bank/_search?q="Virginia"&pretty
解釋:檢索關鍵字爲"Virginia"的結果。結果示例:
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 4.631368,
"hits": [
{
"_index": "bank",
"_type": "account",
"_id": "298",
"_score": 4.631368,
"_source": {
"account_number": 298,
"balance": 34334,
"firstname": "Bullock",
"lastname": "Marsh",
"age": 20,
"gender": "M",
"address": "589 Virginia Place",
"employer": "Renovize",
"email": "[email protected]",
"city": "Coinjock",
"state": "UT"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "25",
"_score": 4.6146765,
"_source": {
"account_number": 25,
"balance": 40540,
"firstname": "Virginia",
"lastname": "Ayala",
"age": 39,
"gender": "F",
"address": "171 Putnam Avenue",
"employer": "Filodyne",
"email": "[email protected]",
"city": "Nicholson",
"state": "PA"
}
}
]
}
}
返回字段含義:
- took – Elasticsearch執行搜索的時間(以毫秒爲單位)
- timed_out – 搜索是否超時
- _shards – 搜索了多少個分片,以及搜索成功/失敗分片的計數
- hits – 搜索結果,是個對象
- hits.total – 符合我們搜索條件的文檔總數
- hits.hits – 實際的搜索結果數組(默認爲前10個文檔)
- hits.sort - 對結果進行排序(如果按score排序則沒有該字段)
- hits._score、max_score - 暫時忽略這些字段
GET /bank/_search?q=*&sort=account_number:asc&pretty
解釋:所有結果通過account_number字段升序排列。默認只返回前10條。
下面的查詢與上面的含義一致:
GET /bank/_search
{
"query": {
"multi_match" : {
"query" : "Virginia",
"fields" : ["_all"]
}
}
}
GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number": "asc" }
]
}
通常我們會採用傳JSON方式查詢。Elasticsearch提供了一種JSON樣式的特定於域的語言,可用於執行查詢。這被稱爲查詢DSL。
注意:上述的查詢裏面我們僅指定了index,並沒有指定type,那麼ES將不會區分type。如果想區分,請在URI後面追加type。示例:GET /bank/account/_search
。
字段檢索
再看按字段查詢:
GET /bank/_search
{
"query": {
"multi_match" : {
"query" : "Virginia",
"fields" : ["firstname"]
}
}
}
GET /bank/_search
{
"query": {
"match" : {
"firstname" : "Virginia"
}
}
}
上面2種查詢是等效的,都是查詢firstname
爲Virginia
的結果。
不分詞
默認檢索都是分詞的,如果我們希望精確匹配,可以這樣實現:
GET /bank/_search
{
"query": {
"match" : {
"address.keyword" : "171 Putnam Avenue"
}
}
}
在字段後面加上.keyword
表示不分詞,使用精確匹配。大家可以測試下面2種查詢結果的區別:
GET /bank/_search
{
"query": {
"match" : {
"address" : "Putnam"
}
}
}
GET /bank/_search
{
"query": {
"match" : {
"address.keyword" : "Putnam"
}
}
}
第二種將查不到任何結果。
分頁
分頁使用關鍵字from、size,分別表示偏移量、分頁大小。
GET /bank/_search
{
"query": { "match_all": {} },
"from": 0,
"size": 2
}
from默認是0,size默認是10。
字段排序
字段排序關鍵字是sort。支持升序(asc)、降序(desc)。
GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number": "asc" }
],
"from":0,
"size":10
}
過濾字段
默認情況下,ES返回所有字段。這被稱爲源(_source
搜索命中中的字段)。如果我們不希望返回所有字段,我們可以只請求返回源中的幾個字段。
GET /bank/_search
{
"query": { "match_all": {} },
"_source": ["account_number", "balance"]
}
通過_source
關鍵字可以實現字段過濾。
AND查詢
如果我們想同時查詢符合A和B字段的結果,該怎麼查呢?可以使用must關鍵字組合。
GET /bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "address": "mill" } },
{ "match": { "address": "lane" } }
]
}
}
}
GET /bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "account_number":136 } },
{ "match": { "address": "lane" } },
{ "match": { "city": "Urie" } }
]
}
}
}
must也等價於:
GET /bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "address": "mill" } }
],
"must": [
{ "match": { "address": "lane" } }
]
}
}
}
這種相當於先查詢A再查詢B,而上面的則是同時查詢符合A和B,但結果是一樣的,執行效率可能有差異。有知道原因的朋友可以告知。
OR查詢
ES使用should關鍵字來實現OR查詢。
GET /bank/_search
{
"query": {
"bool": {
"should": [
{ "match": { "account_number":136 } },
{ "match": { "address": "lane" } },
{ "match": { "city": "Urie" } }
]
}
}
}
AND取反查
must_not
關鍵字實現了既不包含A也不包含B的查詢。
GET /bank/_search
{
"query": {
"bool": {
"must_not": [
{ "match": { "address": "mill" } },
{ "match": { "address": "lane" } }
]
}
}
表示 address 字段需要符合既不包含 mill 也不包含 lane。
布爾組合查詢
我們可以組合 must 、should 、must_not 進行復雜的查詢。
- A AND NOT B
GET /bank/_search
{
"query": {
"bool": {
"must": [
{ "match": { "age": 40 } }
],
"must_not": [
{ "match": { "state": "ID" } }
]
}
}
}
相當於SQL:
select * from bank where age=40 and state!= "ID";
- A AND (B OR C)
GET /bank/_search
{
"query":{
"bool":{
"must":[
{"match":{"age":39}},
{"bool":{"should":[
{"match":{"city":"Nicholson"}},
{"match":{"city":"Yardville"}}
]}
}
]
}
}
}
相當於SQL:
select * from bank where age=39 and (city="Nicholson" or city="Yardville");
範圍查詢
GET /bank/_search
{
"query": {
"bool": {
"must": { "match_all": {} },
"filter": {
"range": {
"balance": {
"gte": 20000,
"lte": 30000
}
}
}
}
}
}
相當於SQL:
select * from bank where balance between 20000 and 30000;
聚合查詢
GET /bank/_search
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword"
}
}
}
}
結果:
{
"took": 29,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped" : 0,
"failed": 0
},
"hits" : {
"total" : 1000,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"group_by_state" : {
"doc_count_error_upper_bound": 20,
"sum_other_doc_count": 770,
"buckets" : [ {
"key" : "ID",
"doc_count" : 27
}, {
"key" : "TX",
"doc_count" : 27
}, {
"key" : "AL",
"doc_count" : 25
}, {
"key" : "MD",
"doc_count" : 25
}, {
"key" : "TN",
"doc_count" : 23
}, {
"key" : "MA",
"doc_count" : 21
}, {
"key" : "NC",
"doc_count" : 21
}, {
"key" : "ND",
"doc_count" : 21
}, {
"key" : "ME",
"doc_count" : 20
}, {
"key" : "MO",
"doc_count" : 20
} ]
}
}
}
查詢結果返回了ID州(Idaho)有27個賬戶,TX州(Texas)有27個賬戶。
相當於SQL:
SELECT state, COUNT(*) FROM bank GROUP BY state ORDER BY COUNT(*) DESC
該查詢意思是按照字段state分組,返回前10個聚合結果。
其中size設置爲0意思是不返回文檔內容,僅返回聚合結果。state.keyword
表示字段精確匹配,因爲使用模糊匹配性能很低,所以不支持。
多重聚合
我們可以在聚合的基礎上再進行聚合,例如求和、求平均值等等。
GET /bank/_search
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword"
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}
上述查詢實現了在前一個聚合的基礎上,按州計算平均帳戶餘額(同樣僅針對按降序排序的前10個州)。
我們可以在聚合中任意嵌套聚合,以從數據中提取所需的統計數據。
在前一個聚合的基礎上,我們現在按降序排列平均餘額:
GET /bank/_search
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword",
"order": {
"average_balance": "desc"
}
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}
這裏基於第二個聚合結果進行倒序排列。其實上一個例子隱藏了默認排序,也就是默認按照_sort
(分值)倒序:
GET /bank/_search
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state.keyword",
"order": {
"_sort": "desc"
}
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}
此示例演示了我們如何按年齡段(20-29歲,30-39歲和40-49歲)進行分組,然後按性別分組,最後得到每個年齡段的平均帳戶餘額:
GET /bank/_search
{
"size": 0,
"aggs": {
"group_by_age": {
"range": {
"field": "age",
"ranges": [
{
"from": 20,
"to": 30
},
{
"from": 30,
"to": 40
},
{
"from": 40,
"to": 50
}
]
},
"aggs": {
"group_by_gender": {
"terms": {
"field": "gender.keyword"
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}
}
}
這個結果就複雜了,屬於嵌套分組,結果也是嵌套的:
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_age": {
"buckets": [
{
"key": "20.0-30.0",
"from": 20,
"to": 30,
"doc_count": 451,
"group_by_gender": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "M",
"doc_count": 232,
"average_balance": {
"value": 27374.05172413793
}
},
{
"key": "F",
"doc_count": 219,
"average_balance": {
"value": 25341.260273972603
}
}
]
}
},
{
"key": "30.0-40.0",
"from": 30,
"to": 40,
"doc_count": 504,
"group_by_gender": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "F",
"doc_count": 253,
"average_balance": {
"value": 25670.869565217392
}
},
{
"key": "M",
"doc_count": 251,
"average_balance": {
"value": 24288.239043824702
}
}
]
}
},
{
"key": "40.0-50.0",
"from": 40,
"to": 50,
"doc_count": 45,
"group_by_gender": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "M",
"doc_count": 24,
"average_balance": {
"value": 26474.958333333332
}
},
{
"key": "F",
"doc_count": 21,
"average_balance": {
"value": 27992.571428571428
}
}
]
}
}
]
}
}
}
term與match查詢
首先大家看下面的例子有什麼區別:
已知條件:ES裏address
爲171 Putnam Avenue
的數據有1條;address
爲Putnam
的數據有0條。index爲bank,type爲account,文檔ID爲25。
GET /bank/_search
{
"query": {
"match" : {
"address" : "Putnam"
}
}
}
GET /bank/_search
{
"query": {
"match" : {
"address.keyword" : "Putnam"
}
}
}
GET /bank/_search
{
"query": {
"term" : {
"address" : "Putnam"
}
}
}
結果:
1、第一個能匹配到數據,因爲會分詞查詢。
2、第二個不能匹配到數據,因爲不分詞的話沒有該條數據。
3、結果不確定。需要看實際是怎麼分詞的。
我們通過下列查詢可以知曉該條數據字段address
的分詞情況:
GET /bank/account/25/_termvectors?fields=address
結果:
{
"_index": "bank",
"_type": "account",
"_id": "25",
"_version": 1,
"found": true,
"took": 0,
"term_vectors": {
"address": {
"field_statistics": {
"sum_doc_freq": 591,
"doc_count": 197,
"sum_ttf": 591
},
"terms": {
"171": {
"term_freq": 1,
"tokens": [
{
"position": 0,
"start_offset": 0,
"end_offset": 3
}
]
},
"avenue": {
"term_freq": 1,
"tokens": [
{
"position": 2,
"start_offset": 11,
"end_offset": 17
}
]
},
"putnam": {
"term_freq": 1,
"tokens": [
{
"position": 1,
"start_offset": 4,
"end_offset": 10
}
]
}
}
}
}
}
可以看出該條數據字段address
一共分了3個詞:
171
avenue
putnam
現在可以得出第三個查詢的答案:匹配不到!但值改成小寫的putnam
又能匹配到了!
原因是:
- term query 查詢的是倒排索引中確切的term
- match query 會對filed進行分詞操作,然後再查詢
由於Putnam
不在分詞裏(大小寫敏感),所以匹配不到。match query先對filed進行分詞,也就是分成putnam
,再去匹配倒排索引中的term,所以能匹配到。
standard
analyzer 分詞器分詞默認會將大寫字母全部轉爲小寫字母。
參考
1、Getting Started | Elasticsearch Reference [5.6] | Elastic
https://www.elastic.co/guide/...
2、Elasticsearch 5.x 關於term query和match query的認識 - wangchuanfu - 博客園
https://www.cnblogs.com/wangc...